DBMS > Atos Standard Common Repository vs. Drizzle vs. Google BigQuery vs. TigerGraph

System Properties Comparison Atos Standard Common Repository vs. Drizzle vs. Google BigQuery vs. TigerGraph

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Editorial information provided by DB-Engines

Name

This system has been discontinued and will be removed from the DB-Engines ranking.

Drizzle has published its last release in September 2012. The open-source project is discontinued and Drizzle is excluded from the DB-Engines ranking.

Description

Highly scalable database system, designed for managing session and subscriber data in modern mobile communication networks

MySQL fork with a pluggable micro-kernel and with an emphasis of performance over compatibility.

Large scale data warehouse service with append-only tables

A complete, distributed, parallel graph computing platform supporting web-scale data analytics in real-time

Primary database model

Document store
Key-value store

Relational DBMS

Graph DBMS

DB-Engines Ranking measures the popularity of database management systems
Trend Chart

Score	60.38
Rank	#19	Overall
	#13	Relational DBMS

Score	1.83
Rank	#139	Overall
	#13	Graph DBMS

Website

atos.net/en/convergence-creators/portfolio/standard-common-repository

cloud.google.com/bigquery

www.tigergraph.com

Technical documentation

cloud.google.com/bigquery/docs

docs.tigergraph.com

Developer

Atos Convergence Creators

Drizzle project, originally started by Brian Aker

Google

Initial release

2016

2008

2010

2017

Current release

1703

7.2.4, September 2012

License

Commercial or Open Source

commercial

Open Source

GNU GPL

commercial

Cloud-based only

Only available as a cloud service

yes

DBaaS offerings (sponsored links)

Database as a Service

Providers of DBaaS offerings, please contact us to be listed.

Implementation language

Java

C++

Server operating systems

Linux

FreeBSD
Linux
OS X

hosted

Linux

Data scheme

Schema and schema-less with LDAP views

yes

Typing

predefined data types such as float or date

optional

yes

XML support

Some form of processing data in XML format, e.g. support for XML data structures, and/or support for XPath, XQuery or XSLT.

yes

Secondary indexes

yes

SQL

Support of SQL

yes

with proprietary extensions

yes

SQL-like query language (GSQL)

APIs and other access methods

LDAP

JDBC

RESTful HTTP/JSON API

GSQL (TigerGraph Query Language)
Kafka
RESTful HTTP/JSON API

Supported programming languages

All languages with LDAP bindings

C
C++
Java
PHP

.Net
Java
JavaScript
Objective-C
PHP
Python
Ruby

C++
Java

Server-side scripts

Stored procedures

user defined functions

in JavaScript

yes

Triggers

yes

hooks for callbacks inside the server can be used.

Partitioning methods

Methods for storing different data on different nodes

Sharding

cell division

Sharding

none

Replication methods

Methods for redundantly storing data on multiple nodes

yes

Multi-source replication
Source-replica replication

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency or Eventual Consistency depending on configuration

Immediate Consistency

Foreign keys

Referential integrity

yes

Relationships in graphs

Transaction concepts

Support to ensure data integrity after non-atomic manipulations of data

Atomic execution of specific operations

ACID

Since BigQuery is designed for querying data

ACID

Concurrency

Support for concurrent manipulation of data

yes

Durability

Support for making data persistent

yes

In-memory capabilities

Is there an option to define some or all structures to be held in-memory only.

yes

User concepts

Access control

LDAP bind authentication

Pluggable authentication mechanisms

e.g. LDAP, HTTP

Access privileges (owner, writer, reader) on dataset, table or view level

Google Cloud Identity & Access Management (IAM)

Role-based access control

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